# cell type annotations
display_df(cell_df_complete)

# organ annotations
display_df(organ_df_complete)

# stage annotations
display_df(stage_df_complete)

experiment_id = "SRP497465"

path_to_create_exp_script = "/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py" 
path_to_barcodes_script = "/Users/scarsana/Desktop/git/scRNA-Seq/scripts/create_scRNASeq_barcode_v2.py"
experiment_type = "scRNA"
import os


path_to_output_main = "/Users/scarsana/Desktop/git/expression-annotations/Notebooks/scRNA/" 
path_to_output = "{}{}/".format(path_to_output_main, experiment_id)

path_to_initial_folder = "{}initial/".format(path_to_output)
if not os.path.exists(path_to_initial_folder):
    os.makedirs(path_to_initial_folder)

library_path_from_script = "{}scRNASeqLibrary_{}.tsv".format(path_to_initial_folder, experiment_id)
experiment_path_from_script = "{}scRNASeqExperiment_{}.tsv".format(path_to_initial_folder, experiment_id)
barcode_path = "{}scRNASeq_barcode_{}.tsv".format(path_to_initial_folder, experiment_id)


path_to_complete_folder = "{}complete/".format(path_to_output)
if not os.path.exists(path_to_complete_folder):
    os.makedirs(path_to_complete_folder)

barcode_to_add_path = "{}complete_scRNASeq_barcode_{}.tsv".format(path_to_complete_folder, experiment_id)
library_to_add_path = "{}complete_scRNASeqLibrary_{}.tsv".format(path_to_complete_folder, experiment_id)
experiment_to_add_path = "{}complete_RNASeqExperiment_{}.tsv".format(path_to_complete_folder, experiment_id)
script_file = "{}.ipynb".format(experiment_id)
commit_message_exp = '"adding annotated scRNA experiment {}"'.format(experiment_id)
commit_message_py = '"adding annotation files for {} to notebook folder"'.format(experiment_id)


## to add to git
path_to_git_annotations = "/Users/scarsana/Desktop/git/expression-annotations/scRNA_Seq/"
git_library_path = "{}scRNASeqLibrary_merged.tsv".format(path_to_git_annotations)
git_experiment_path = "{}scRNASeqExperiment.tsv".format(path_to_git_annotations)
git_barcode_path = "{}scRNASeq_barcode_{}.tsv".format(path_to_git_annotations, experiment_id)

## columns
library_cols = ['#libraryId', 'experimentId', 'platform', 'SRSId', 'anatId', 'anatName', 'cellTypeId', 'cellTypeName', 'stageId', 'stageName', 'url_GSM', 'infoOrgan', 'infoCellType_abInitio', 'infoCellType_inferred', 'clusterId', 'clusterName', 'infoStage', 'anatAnnotationStatus', 'cellTypeAnnotationStatus', 'stageAnnotationStatus', 'sex', 'strain', 'genotype', 'speciesId', 'RNAseqTags', 'protocol', 'protocolType', 'lib_name', 'sampleName', 'comment', 'condition', 'annotatorId', 'lastModificationDate']
barcode_cols = ['barcode', 'cluster', 'library', 'experiment', 'tissue', 'cell_type', 'anatId_a_posteriori', 'anatName_a_posteriori', 'anat_a_posteriori_annotationStatus', 'cellTypeId', 'cellTypeName', 'cellTypeAnnotationStatus', 'name_Library', 'comments']

## annotation sheet paths (maybe make these into their own folder)
annotation_files_path = "{}annotation_files/".format(path_to_output)

if not os.path.exists(annotation_files_path):
    os.makedirs(annotation_files_path)

info_anat_path = "{}info_anat.tsv".format(annotation_files_path)
info_anat_complete_path = "{}info_anat_complete.tsv".format(annotation_files_path)
info_stage_path = "{}info_dev_stage.tsv".format(annotation_files_path)
info_stage_complete_path = "{}info_dev_stage_complete.tsv".format(annotation_files_path)
info_celltype_path = "{}info_celltype.tsv".format(annotation_files_path)
info_celltype_complete_path = "{}info_celltype_complete.tsv".format(annotation_files_path)

## barcode file & matching library names - 10X only
path_to_barcode_file = "{}barcode_{}.tsv".format(path_to_output, experiment_id)
lib_names_path = "{}matching_lib_names_{}.tsv".format(path_to_initial_folder, experiment_id)

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import numpy as np
from IPython.display import display, HTML
import csv
import scanpy as sc

# displays df with the scrollbar next to the DataFrame
def display_df(df):
    pd.set_option("display.max_rows", None)
    pd.set_option("display.max_columns", None)
    display(HTML("<div style='height: 200px; overflow: auto; width: fit-content'>" +
        df.style.to_html(index=False) + "</div>"))

# function that compares two columns in a dataframe and tells you which ones are not equal (case insensitive)
def compare_columns(df, col1, col2, return_col):
    compare_return = df[col1].str.lower() != df[col2].str.lower()  
    df.loc[compare_return, return_col] 
    if not any(compare_return):
        print("The two columns are equal (case insensitive)")
    else:
        print("The following rows are not equal: ")
        print(df.loc[compare_return, [return_col, col1, col2]])

# fixes formatting of file to match libreoffice settings/historic file format
def update_format(path):
    with open(path, 'r') as file:
        filedata = file.read()
    # Replace the target string
    filedata = filedata.replace("\t\"\"", "\t")
    # Write the file out again
    with open(path, 'w') as file:
        file.write(filedata)

# checks for duplicate values in a specific column and prints those values + the corresponding library id
def dup_check(df, column):
    duplicateCheck = df.duplicated(subset=[column], keep=False)
    duplicateCheck.sort_values(inplace=True)
    if duplicateCheck.unique().any() == False:
        print("no duplicate values in " + column)
    elif duplicateCheck.unique().any() == True and column != '#libraryId':
        dups = df[duplicateCheck].loc[:,['#libraryId', column]]
        df_dups = pd.DataFrame(dups)
        df_dups.sort_values(inplace=True, by=column)
        print(df_dups)
    elif duplicateCheck.unique().any() == True and column == '#libraryId':
        print(df[duplicateCheck].loc[:,['#libraryId']])

# prints all unique values in a specific column
def unique_sorted(df, column):
    unique = df[column].unique()
    unique.sort()
    print(unique)

/var/folders/b5/crkp117d43q5mcndnwlrww3w0000gn/T/ipykernel_57035/2872653486.py:3: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd

! python3 $path_to_create_exp_script $experiment_id $path_to_initial_folder $experiment_type

/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py:97: SyntaxWarning: invalid escape sequence '\('
  all_protoc = [w.replace('(', '\(') for w in all_protoc]
/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py:98: SyntaxWarning: invalid escape sequence '\)'
  all_protoc = [w.replace(')', '\)') for w in all_protoc] 
/Users/scarsana/Desktop/git/scRNA-Seq/scripts/Create_ExpLib_tables.py:11: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd
Be patient, it may take a few minutes.
0it [00:00, ?it/s]
0 samples dont have attributes, try to find them somewhere else
0it [00:00, ?it/s]
0 samples dont have attributes

library = pd.read_csv(library_path_from_script, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(library)

infoOrgan = library['infoOrgan'].drop_duplicates().sort_values()
organ_df = pd.DataFrame(infoOrgan)
organ_df = organ_df.reindex(columns=[*organ_df.columns.tolist(), 'notes_anat', 'anatId_', 'anatName_', 'anatAnnotationStatus_', 'infoOrgan_new', 'comment_anat'], fill_value="")
organ_df.to_csv(info_anat_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
if not os.path.exists(info_anat_complete_path): 
    organ_df.to_csv(info_anat_complete_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

# options for anatAnnotationStatus: perfect match, missing child term, other

organ_df_complete = pd.read_csv(info_anat_complete_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(organ_df_complete)

library = library.merge(organ_df_complete, left_on='infoOrgan', right_on='infoOrgan')

library['anatId'] = library['anatId_'].values
library['anatName'] = library['anatName_'].values
library['anatAnnotationStatus'] = library['anatAnnotationStatus_'].values

## optional - update infoOrgan, add anat-specific comments 
#library['infoOrgan'] = library['infoOrgan_new'].values
#library['comment'] = library[['comment', 'comment_anat']].agg(' '.join, axis=1)

# reformat, save progress
library = library[library_cols]
display_df(library)
library.to_csv(library_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

infoStage = library['infoStage'].drop_duplicates().sort_values()
stage_df = pd.DataFrame(infoStage)
stage_df = stage_df.reindex(columns=[*stage_df.columns.tolist(), 'notes_stage', 'stageId_', 'stageName_', 'stageAnnotationStatus_', 'infoStage_new','comment_stage'], fill_value="")
stage_df.to_csv(info_stage_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
if not os.path.exists(info_stage_complete_path): 
    stage_df.to_csv(info_stage_complete_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

# options for stageAnnotationStatus: perfect match, missing child term, other

stage_df_complete = pd.read_csv(info_stage_complete_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(stage_df_complete)

library = library.merge(stage_df_complete, left_on='infoStage', right_on='infoStage')

library['stageId'] = library['stageId_'].values
library['stageName'] = library['stageName_'].values
library['stageAnnotationStatus'] = library['stageAnnotationStatus_'].values

## optional - update infoOrgan, add anat-specific comments 
#library['infoStage'] = library['infoStage_new'].values
#library['comment'] = library[['comment', 'comment_stage']].agg(' '.join, axis=1)

# reformat, save progress
library = library[library_cols]
display_df(library)
library.to_csv(library_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

#library.loc[library["sex"] == "male", "sex"] = "M"
#library.loc[library["sex"] == "female", "sex"] = "F"

#library.loc[:,'strain'] = ''

#library.loc[:,'genotype'] = ''

#library.loc[:,'speciesId'] = ''

# view
display_df(library)

# making these variables because we use them again in the experiment file
# usually 10X Genomics V1/V2/V3/V3.1, Smart-seq, Smart-seq2
my_protocol = '10X Genomics Multiome V1'
# Full-length or 3'end or 5'end
my_protocolType = "3'end"

library.loc[:,'protocol'] = my_protocol
library.loc[:,'protocolType'] = my_protocolType
# scRNA-seq, Sn-scRNA-seq
library.loc[:,'RNAseqTags'] = 'scRNA-seq'

# view
display_df(library)

# ex. control, diet, light, reproductive capacity, time post mortem, time post feeding, 
# exercise details, menstruation, personality, litter size 
#library.loc[library["condition"] == "old", "condition"] = "new"

# view
display_df(library)

library.loc[:,'annotatorId'] = 'SAC'
library.loc[:,'lastModificationDate'] = '2025-03-04'

# view
display_df(library)

library.loc[:,'comment'] = 'chickens are male, broiler, 15 days old per extended methods'

library_file_complete = library[library_cols]

library_file_complete.to_csv(library_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

# view
display_df(library_file_complete)

barcode_column = "cell_id"
library_column = "sample_name"
cell_type_column = "description_celltype"

! python3 $path_to_barcodes_script $path_to_initial_folder --scRNASeqLibrary $library_to_add_path --barcode_file $path_to_barcode_file --barcode_col $barcode_column --libName_col $library_column --cellType_col $cell_type_column --ignore_check --no_additional_file

/Users/scarsana/Desktop/git/scRNA-Seq/scripts/create_scRNASeq_barcode_v2.py:10: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd
There are 2 unique libraries in scRNASeqLibrary.tsv table
There are 2 unique libraries in the barcode table provided
Be extra careful if not same number of libraries in both tables, always manually check the matching names in 'matching_lib_names.tsv' table output
100%|███████████████████████████████████████████| 2/2 [00:00<00:00, 9127.97it/s]
                       name_Library                  new_lib_name
9116   sample_1_whole_telencephalon  sample_1_whole_telencephalon
12392  sample_1_whole_telencephalon  sample_1_whole_telencephalon
14998      sample_2_coronal_section      sample_2_coronal_section
6103   sample_1_whole_telencephalon  sample_1_whole_telencephalon
12894  sample_1_whole_telencephalon  sample_1_whole_telencephalon
8370   sample_1_whole_telencephalon  sample_1_whole_telencephalon
7801   sample_1_whole_telencephalon  sample_1_whole_telencephalon
22841      sample_2_coronal_section      sample_2_coronal_section
22939      sample_2_coronal_section      sample_2_coronal_section
1954   sample_1_whole_telencephalon  sample_1_whole_telencephalon
151    sample_1_whole_telencephalon  sample_1_whole_telencephalon
12565  sample_1_whole_telencephalon  sample_1_whole_telencephalon
23625      sample_2_coronal_section      sample_2_coronal_section
7067   sample_1_whole_telencephalon  sample_1_whole_telencephalon
2782   sample_1_whole_telencephalon  sample_1_whole_telencephalon
12437  sample_1_whole_telencephalon  sample_1_whole_telencephalon
7231   sample_1_whole_telencephalon  sample_1_whole_telencephalon
15658      sample_2_coronal_section      sample_2_coronal_section
12572  sample_1_whole_telencephalon  sample_1_whole_telencephalon
14082      sample_2_coronal_section      sample_2_coronal_section
0 libraries dont have any barcodes !!
the following libraries don't have any barcodes: set()

lib_names = pd.read_csv(lib_names_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(lib_names)

barcode_file = pd.read_csv(barcode_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

cell_type = barcode_file['cell_type'].drop_duplicates().sort_values()
cell_df = pd.DataFrame(cell_type)
cell_df = cell_df.reindex(columns=[*cell_df.columns.tolist(), 'notes_cell', 'cellTypeId_', 'cellTypeName_', 'cellTypeAnnotationStatus_', 'cell_type_new','comment_cell'], fill_value="")
cell_df.to_csv(info_celltype_path, sep="\t", index=False)
if not os.path.exists(info_celltype_complete_path): 
    cell_df.to_csv(info_celltype_complete_path, sep="\t", index=False)

# options for cellTypeAnnotationStatus: perfect match, missing child term, other

cell_df_complete = pd.read_csv(info_celltype_complete_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
display_df(cell_df_complete)

barcode_file = barcode_file.merge(cell_df_complete, left_on='cell_type', right_on='cell_type')

barcode_file['cellTypeId'] = barcode_file['cellTypeId_'].values
barcode_file['cellTypeName'] = barcode_file['cellTypeName_'].values
barcode_file['cellTypeAnnotationStatus'] = barcode_file['cellTypeAnnotationStatus_'].values

# optional - update cell type name and add comments
#barcode_file['cell_type'] = barcode_file['cell_type_new'].values
#barcode_file['comments'] = barcode_file[['comment_cell']]

barcode_file_complete = barcode_file[barcode_cols]
display_df(barcode_file_complete)
barcode_file_complete.to_csv(barcode_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

experiment = pd.read_csv(experiment_path_from_script, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
experiment.head()

library_to_add = pd.read_csv(library_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

# this will give you the number of rows in the complete library file 
# this should be the number of annotated libraries
ann_lib = len(library_to_add.index)
len(library_to_add.index)

2

# partial or total
experiment.loc[:,'experimentStatus'] = 'partial'
# scRNA-seq, Sn-scRNA-seq for both we put: 'Sn-scRNA-seq, scRNA-seq'
experiment.loc[:,'RNAseqTags'] = 'scRNA-seq'


# see above cell, also can add as free text
experiment.loc[:,'numberOfAnnotatedLibraries'] = ann_lib

# these variables should already exist from above but if not can just add as free text
experiment.loc[:,'protocol'] = my_protocol
experiment.loc[:,'protocolType'] = my_protocolType

experiment.head()

experiment.loc[:,'GSE'] = 'GSE262321'
#experiment.loc[:,'Bioproject'] = '' 
experiment.loc[:,'PMID'] = '39946451'
experiment.loc[:,'reference_url'] = 'https://www.science.org/doi/10.1126/science.adp3957'
experiment.loc[:,'DOI'] = '10.1126/science.adp3957'
#experiment.loc[:,'xrefs'] = ''

experiment.head()

experiment.loc[:,'comment'] = 'partial because i removed ATAC libraries. cell barcodes from GEO GSE262321_meta_data.tsv.gz, extended methods have age, sex and strain'

experiment.head()

experiment.to_csv(experiment_to_add_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)

library_to_add = pd.read_csv(library_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
experiment_to_add = pd.read_csv(experiment_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

# pull from git and pull in library/experiment file
! git pull
git_library = pd.read_csv(git_library_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
git_experiment = pd.read_csv(git_experiment_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)

# library file
if set(library_to_add.columns) == set(git_library.columns):
    print('The columns in the library file match')
else:
    print('The columns in the library file DO NOT MATCH')

# experiment file
if set(experiment_to_add.columns) == set(git_experiment.columns):
    print('The columns in the experiment file match')
else:
    print('The columns in the experiment file DO NOT MATCH')


# maybe to make this something more like "COLUMNS GOOD - LIBRARY" and "COLUMNS BAD - EXPERIMENT"

Already up to date.
The columns in the library file match
The columns in the experiment file match

library_git_plus_new = pd.concat([git_library, library_to_add], ignore_index = True, sort = False)
old_length = git_library.shape[0]
start = old_length - 2
end = old_length + 5
view_lib = library_git_plus_new.iloc[start:end]
view_lib

experiment_git_plus_new = pd.concat([git_experiment, experiment_to_add], ignore_index = True, sort = False)
experiment_git_plus_new.tail(n=3)

# 10X only
barcode_file_for_git = pd.read_csv(barcode_to_add_path, sep='\t', index_col=False, keep_default_na=False, na_values=['NULL','null', 'nan','NaN', " "], dtype=object)
barcode_file_for_git.head(10)

! git pull

Already up to date.

library_git_plus_new.to_csv(git_library_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
update_format(git_library_path)
experiment_git_plus_new.to_csv(git_experiment_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
update_format(git_experiment_path)

# 10X only
barcode_file_for_git.to_csv(git_barcode_path, sep="\t", index=False, quoting=csv.QUOTE_ALL)
update_format(git_barcode_path)

! git status

On branch develop
Your branch is up to date with 'origin/develop'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   ../../../scRNA_Seq/scRNASeqExperiment.tsv
	modified:   ../../../scRNA_Seq/scRNASeqLibrary_merged.tsv

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	./
	../SRP537812_waiting/
	../../../scRNA_Seq/scRNASeq_barcode_SRP497465.tsv

no changes added to commit (use "git add" and/or "git commit -a")

# 10X (barcode file)
! git add $git_experiment_path $git_library_path $git_barcode_path

! git status

On branch develop
Your branch is up to date with 'origin/develop'.

Changes to be committed:
  (use "git restore --staged <file>..." to unstage)
	modified:   ../../../scRNA_Seq/scRNASeqExperiment.tsv
	modified:   ../../../scRNA_Seq/scRNASeqLibrary_merged.tsv
	new file:   ../../../scRNA_Seq/scRNASeq_barcode_SRP497465.tsv

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	./
	../SRP537812_waiting/

! git commit -m $commit_message_exp

[develop 1b938b7] adding annotated scRNA experiment SRP497465
 3 files changed, 24492 insertions(+)
 create mode 100644 scRNA_Seq/scRNASeq_barcode_SRP497465.tsv

! git push

Enumerating objects: 10, done.
Counting objects: 100% (10/10), done.
Delta compression using up to 12 threads
Compressing objects: 100% (6/6), done.
Writing objects: 100% (6/6), 193.67 KiB | 6.25 MiB/s, done.
Total 6 (delta 4), reused 0 (delta 0), pack-reused 0
remote: 
remote: To create a merge request for develop, visit:
remote:   https://gitlab.sib.swiss/Bgee/expression-annotations/-/merge_requests/new?merge_request%5Bsource_branch%5D=develop
remote: 
To https://gitlab.sib.swiss/Bgee/expression-annotations.git
   62038ae..1b938b7  develop -> develop

! git status

! git add $path_to_output

! git commit -m $commit_message_py

! git push

	cell_type	notes_cell	cellTypeId_	cellTypeName_	cellTypeAnnotationStatus_
0	Astrocyte		CL:0000127	astrocyte	perfect match
1	D1 medium spiny neuron		CL:4023026	direct pathway medium spiny neuron	perfect match
2	D2 medium spiny neuron		CL:4023029	indirect pathway medium spiny neuron	perfect match
3	Endothelial vascualr cell	most correct term here is endothelial cell of vascular tree, although blood vessel endothelial cell is likely also correct here. exact synonym of endothelial cell of vascular tree is vascular endothelial cell and the term is fully inclusive of blood and lymphatic vessel endothelial cells, going to endothelial cell of vascular tree	CL:0002139	endothelial cell of vascular tree	perfect match
4	Excitatory neuron cluster 1	going with glutamatergic neuron for these but some additional info from the paper: EXC-1 and EXC-2 show similarities to mammalian intratelencephalic (IT) neurons of cortical layers L2 and L3 (L2/3 IT) and L4 (L4 IT); excitatory neurons in the L2/3 IT piriform cortex (PIR); and the cortical, medial, and lateral amygdala (COA, MEA, and LA, respectively)	CL:0000679	glutamatergic neuron	perfect match
5	Excitatory neuron cluster 2	going with glutamatergic neuron for these but some additional info from the paper: EXC-1 and EXC-2 show similarities to mammalian intratelencephalic (IT) neurons of cortical layers L2 and L3 (L2/3 IT) and L4 (L4 IT); excitatory neurons in the L2/3 IT piriform cortex (PIR); and the cortical, medial, and lateral amygdala (COA, MEA, and LA, respectively)	CL:0000679	glutamatergic neuron	perfect match
6	Excitatory neuron cluster 3	going with glutamatergic neuron for these but some additional info from the paper: the entopallial RORB+ excitatory neurons (EXC-3) have a moderate similarity to mammalian L4 IT neurons	CL:0000679	glutamatergic neuron	perfect match
7	Excitatory neuron cluster 4	going with glutamatergic neuron for these but some additional info from the paper: Excitatory neurons of the mesopallium (EXC-4, -5, and -7) show the highest resemblances to the mammalian deeper-layer cortical neurons, including L5/6 IT neurons, L6 corticothalamic (CT) neurons, L6b, and neurons of the claustrum (CLA) and CT neurons of the subiculum	CL:0000679	glutamatergic neuron	perfect match
8	Excitatory neuron cluster 5	going with glutamatergic neuron for these but some additional info from the paper: Excitatory neurons of the mesopallium (EXC-4, -5, and -7) show the highest resemblances to the mammalian deeper-layer cortical neurons, including L5/6 IT neurons, L6 corticothalamic (CT) neurons, L6b, and neurons of the claustrum (CLA) and CT neurons of the subiculum	CL:0000679	glutamatergic neuron	perfect match
9	Excitatory neuron cluster 6	going with glutamatergic neuron for these but some additional info from the paper: Medial pallium neurons (EXC-6) are most similar to mammalian hippocampal cornu ammonis (CA) neurons and the amygdala	CL:0000679	glutamatergic neuron	perfect match
10	Excitatory neuron cluster 7	going with glutamatergic neuron for these but some additional info from the paper: Excitatory neurons of the mesopallium (EXC-4, -5, and -7) show the highest resemblances to the mammalian deeper-layer cortical neurons, including L5/6 IT neurons, L6 corticothalamic (CT) neurons, L6b, and neurons of the claustrum (CLA) and CT neurons of the subiculum	CL:0000679	glutamatergic neuron	perfect match
11	Excitatory neuron cluster 8	no extra info from paper on this one, still going with glutamatergic neuron	CL:0000679	glutamatergic neuron	perfect match
12	Inhibitory neuron cluster 1	no extra info just inhibitory neuron, not even interneuron so going general GABA	CL:0000617	GABAergic neuron	perfect match
13	Inhibitory neuron cluster 2	no extra info just inhibitory neuron, not even interneuron so going general GABA	CL:0000617	GABAergic neuron	perfect match
14	Intermediate oligodendrocyte	it's weird because it calls IOLs Intermediate oligodendrocyte in the spreadsheets, but it calls them immature oligodendrocytes in the paper. I think these are all different names for COPs (committed oligodendrocyte precursors) - they are also called intermediate OLs since they are intermediate between OPC and OL. COP annotation is good here, but giving annotation status other since the naming convention here is weird	CL:4023059	differentiation-committed oligodendrocyte precursor	other
15	LAMP5 interneuron		CL:4023011	lamp5 GABAergic cortical interneuron	perfect match
16	Microglia		CL:0000129	microglial cell	perfect match
17	Oligodendrocyte		CL:0000128	oligodendrocyte	perfect match
18	Oligodendrocyte precursor cell		CL:0002453	oligodendrocyte precursor cell	perfect match
19	PVALB interneuron		CL:4023018	pvalb GABAergic cortical interneuron	perfect match
20	SST interneuron		CL:4023017	sst GABAergic cortical interneuron	perfect match
21	Striatal-like inhibitory neuron	in CL the most relevant terms are GABAergic neuron (inhibitory) or striatum neuron. methods sometimes call these Striatum derived interneurons, however I think that is specific to the human and mouse set. if this included marker genes / DEGs or some additional information i think we could narrow it down to a STR interneuron subtype, however with the information we have here I think here it makes more sense to go GABAergic neuron since it's not even official striatal neurons, it's striatal-like	CL:0000617	GABAergic neuron	missing child term
22	Unannotated Cluster 16	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
23	Unannotated Cluster 18	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
24	Unannotated Cluster 23	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
25	Unannotated Cluster 27	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
26	Unannotated Cluster 28	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
27	Unannotated Cluster 30	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
28	Unannotated Cluster 33	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
29	VIP interneuron		CL:4023016	VIP GABAergic cortical interneuron	perfect match

	cell_type	notes_cell	cellTypeId_	cellTypeName_	cellTypeAnnotationStatus_
0	Astrocyte		CL:0000127	astrocyte	perfect match
1	D1 medium spiny neuron		CL:4023026	direct pathway medium spiny neuron	perfect match
2	D2 medium spiny neuron		CL:4023029	indirect pathway medium spiny neuron	perfect match
3	Endothelial vascualr cell	most correct term here is endothelial cell of vascular tree, although blood vessel endothelial cell is likely also correct here. exact synonym of endothelial cell of vascular tree is vascular endothelial cell and the term is fully inclusive of blood and lymphatic vessel endothelial cells, going to endothelial cell of vascular tree	CL:0002139	endothelial cell of vascular tree	perfect match
4	Excitatory neuron cluster 1	going with glutamatergic neuron for these but some additional info from the paper: EXC-1 and EXC-2 show similarities to mammalian intratelencephalic (IT) neurons of cortical layers L2 and L3 (L2/3 IT) and L4 (L4 IT); excitatory neurons in the L2/3 IT piriform cortex (PIR); and the cortical, medial, and lateral amygdala (COA, MEA, and LA, respectively)	CL:0000679	glutamatergic neuron	perfect match
5	Excitatory neuron cluster 2	going with glutamatergic neuron for these but some additional info from the paper: EXC-1 and EXC-2 show similarities to mammalian intratelencephalic (IT) neurons of cortical layers L2 and L3 (L2/3 IT) and L4 (L4 IT); excitatory neurons in the L2/3 IT piriform cortex (PIR); and the cortical, medial, and lateral amygdala (COA, MEA, and LA, respectively)	CL:0000679	glutamatergic neuron	perfect match
6	Excitatory neuron cluster 3	going with glutamatergic neuron for these but some additional info from the paper: the entopallial RORB+ excitatory neurons (EXC-3) have a moderate similarity to mammalian L4 IT neurons	CL:0000679	glutamatergic neuron	perfect match
7	Excitatory neuron cluster 4	going with glutamatergic neuron for these but some additional info from the paper: Excitatory neurons of the mesopallium (EXC-4, -5, and -7) show the highest resemblances to the mammalian deeper-layer cortical neurons, including L5/6 IT neurons, L6 corticothalamic (CT) neurons, L6b, and neurons of the claustrum (CLA) and CT neurons of the subiculum	CL:0000679	glutamatergic neuron	perfect match
8	Excitatory neuron cluster 5	going with glutamatergic neuron for these but some additional info from the paper: Excitatory neurons of the mesopallium (EXC-4, -5, and -7) show the highest resemblances to the mammalian deeper-layer cortical neurons, including L5/6 IT neurons, L6 corticothalamic (CT) neurons, L6b, and neurons of the claustrum (CLA) and CT neurons of the subiculum	CL:0000679	glutamatergic neuron	perfect match
9	Excitatory neuron cluster 6	going with glutamatergic neuron for these but some additional info from the paper: Medial pallium neurons (EXC-6) are most similar to mammalian hippocampal cornu ammonis (CA) neurons and the amygdala	CL:0000679	glutamatergic neuron	perfect match
10	Excitatory neuron cluster 7	going with glutamatergic neuron for these but some additional info from the paper: Excitatory neurons of the mesopallium (EXC-4, -5, and -7) show the highest resemblances to the mammalian deeper-layer cortical neurons, including L5/6 IT neurons, L6 corticothalamic (CT) neurons, L6b, and neurons of the claustrum (CLA) and CT neurons of the subiculum	CL:0000679	glutamatergic neuron	perfect match
11	Excitatory neuron cluster 8	no extra info from paper on this one, still going with glutamatergic neuron	CL:0000679	glutamatergic neuron	perfect match
12	Inhibitory neuron cluster 1	no extra info just inhibitory neuron, not even interneuron so going general GABA	CL:0000617	GABAergic neuron	perfect match
13	Inhibitory neuron cluster 2	no extra info just inhibitory neuron, not even interneuron so going general GABA	CL:0000617	GABAergic neuron	perfect match
14	Intermediate oligodendrocyte	it's weird because it calls IOLs Intermediate oligodendrocyte in the spreadsheets, but it calls them immature oligodendrocytes in the paper. I think these are all different names for COPs (committed oligodendrocyte precursors) - they are also called intermediate OLs since they are intermediate between OPC and OL. COP annotation is good here, but giving annotation status other since the naming convention here is weird	CL:4023059	differentiation-committed oligodendrocyte precursor	other
15	LAMP5 interneuron		CL:4023011	lamp5 GABAergic cortical interneuron	perfect match
16	Microglia		CL:0000129	microglial cell	perfect match
17	Oligodendrocyte		CL:0000128	oligodendrocyte	perfect match
18	Oligodendrocyte precursor cell		CL:0002453	oligodendrocyte precursor cell	perfect match
19	PVALB interneuron		CL:4023018	pvalb GABAergic cortical interneuron	perfect match
20	SST interneuron		CL:4023017	sst GABAergic cortical interneuron	perfect match
21	Striatal-like inhibitory neuron	in CL the most relevant terms are GABAergic neuron (inhibitory) or striatum neuron. methods sometimes call these Striatum derived interneurons, however I think that is specific to the human and mouse set. if this included marker genes / DEGs or some additional information i think we could narrow it down to a STR interneuron subtype, however with the information we have here I think here it makes more sense to go GABAergic neuron since it's not even official striatal neurons, it's striatal-like	CL:0000617	GABAergic neuron	missing child term
22	Unannotated Cluster 16	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
23	Unannotated Cluster 18	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
24	Unannotated Cluster 23	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
25	Unannotated Cluster 27	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
26	Unannotated Cluster 28	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
27	Unannotated Cluster 30	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
28	Unannotated Cluster 33	unannotated clusters get root term annotation	CL:0000000	cell	perfect match
29	VIP interneuron		CL:4023016	VIP GABAergic cortical interneuron	perfect match

	#libraryId	experimentId	platform	SRSId	anatId	anatName	cellTypeId	cellTypeName	stageId	stageName	url_GSM	infoOrgan	infoCellType_abInitio	infoStage	anatAnnotationStatus	cellTypeAnnotationStatus	stageAnnotationStatus	sex	strain	speciesId	RNAseqTags	protocol	protocolType	lib_name	sampleName	comment	annotatorId	lastModificationDate
59621	SRX23311390	SRP484717	Illumina NovaSeq 6000	SRS20187720	UBERON:0000358	blastocyst	CL:0000352	epiblast cell	UBERON:0000108	blastula stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Late blastocyst: E5.75	ICM: EPI	Late blastocyst: E5.75	missing child term	perfect match	missing child term	NA	Hartley	10141	scRNA-seq	Smart-Seq2	Full-length	JC_2_98 (21PG55-3)	SAMN39503388	no PMID but DOI: 10.1101/2024.02.05.578945, a...	SAC	2025-02-20
59622	SRX23311391	SRP484717	Illumina NovaSeq 6000	SRS20187717	UBERON:0000358	blastocyst	CL:0000352	epiblast cell	UBERON:0000108	blastula stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Late blastocyst: E5.75	ICM: EPI	Late blastocyst: E5.75	missing child term	perfect match	missing child term	NA	Hartley	10141	scRNA-seq	Smart-Seq2	Full-length	JC_2_99 (21PG55-3)	SAMN39503387	no PMID but DOI: 10.1101/2024.02.05.578945, a...	SAC	2025-02-20
59623	SRX24035191	SRP497465	NextSeq 2000	SRS20828625	UBERON:0001893	telencephalon			GgalDv:0000073	2-week-old stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Telencephalon - coronal section		15 days	perfect match		missing child term	M	Broiler	9031	scRNA-seq	10X Genomics Multiome V1	3'end	sample_2_coronal_section	SAMN40593971	chickens are male, broiler, 15 days old per ex...	SAC	2025-03-04
59624	SRX24035190	SRP497465	NextSeq 2000	SRS20828627	UBERON:0001893	telencephalon			GgalDv:0000073	2-week-old stage	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi...	Telencephalon - whole telencephalon		15 days	perfect match		missing child term	M	Broiler	9031	scRNA-seq	10X Genomics Multiome V1	3'end	sample_1_whole_telencephalon	SAMN40593972	chickens are male, broiler, 15 days old per ex...	SAC	2025-03-04

	#experimentId	experimentName	experimentDescription	experimentSource	experimentStatus	RNAseqTags	numberOfAnnotatedLibraries	protocol	protocolType	GSE	Bioproject	PMID	reference_url	DOI	xrefs	comment
110	ERP136630	This is a repository for all raw data of singl...	The transcriptomic profile and the composition...	ENA	partial	scRNA-seq	5	10X Genomics V3.1	3'end		PRJEB51962	38153159	https://onlinelibrary.wiley.com/doi/10.1111/im...	10.1111/imm.13745	36292607[PMID], 36105804[PMID]	removed asthmatic samples and one pacbio isose...
111	SRP484717	The Guinea Pig Serves as an Alternative Model ...	Preimplantation development is a pivotal phase...	SRA	partial	scRNA-seq	541	Smart-Seq2	Full-length	GSE253670	PRJNA1066746		https://www.biorxiv.org/content/10.1101/2024.0...	10.1101/2024.02.05.578945		no PMID because paper is still in preprint alt...
112	SRP497465	Deep learning of enhancer codes highlights sim...	Combinations of transcription factors govern t...	SRA	partial	scRNA-seq	2	10X Genomics Multiome V1	3'end	GSE262321	PRJNA1091336	39946451	https://www.science.org/doi/10.1126/science.ad...	10.1126/science.adp3957		partial because i removed ATAC libraries. cell...

	barcode	library	experiment	tissue	cell_type	cellTypeId	cellTypeName	cellTypeAnnotationStatus	name_Library
0	AAACAGCCACCACAAC	SRX24035190	SRP497465	Telencephalon - whole telencephalon	VIP interneuron	CL:4023016	VIP GABAergic cortical interneuron	perfect match	sample_1_whole_telencephalon
1	AAACAGCCACCTACGG	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Striatal-like inhibitory neuron	CL:0000617	GABAergic neuron	missing child term	sample_1_whole_telencephalon
2	AAACAGCCAGGACACA	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Unannotated Cluster 16	CL:0000000	cell	perfect match	sample_1_whole_telencephalon
3	AAACATGCACATGCTA	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Excitatory neuron cluster 2	CL:0000679	glutamatergic neuron	perfect match	sample_1_whole_telencephalon
4	AAACATGCACCGGCTA	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Striatal-like inhibitory neuron	CL:0000617	GABAergic neuron	missing child term	sample_1_whole_telencephalon
5	AAACATGCACTGGCTG	SRX24035190	SRP497465	Telencephalon - whole telencephalon	D2 medium spiny neuron	CL:4023029	indirect pathway medium spiny neuron	perfect match	sample_1_whole_telencephalon
6	AAACATGCAGCTACGT	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Oligodendrocyte precursor cell	CL:0002453	oligodendrocyte precursor cell	perfect match	sample_1_whole_telencephalon
7	AAACATGCATAATGTC	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Excitatory neuron cluster 2	CL:0000679	glutamatergic neuron	perfect match	sample_1_whole_telencephalon
8	AAACATGCATTATGAC	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Excitatory neuron cluster 7	CL:0000679	glutamatergic neuron	perfect match	sample_1_whole_telencephalon
9	AAACCAACAATTAGGA	SRX24035190	SRP497465	Telencephalon - whole telencephalon	Microglia	CL:0000129	microglial cell	perfect match	sample_1_whole_telencephalon

SRP497465 / GSE262321¶

annotation summary¶

set variables, import packages, define functions¶

script¶

library annnotations¶

anatomical entity¶

stage¶

sex, strain, genotype, speciesId¶

protocol & RNAseqTags¶

condition¶

annotator id, last modification date¶

comments¶

save complete file with correct columns¶

script - barcode (10X only)¶

check library names match¶

cell type annotation - 10X version¶

experiment annotations¶

experiment and protocol details¶

paper and xrefs¶

comments¶

save complete file¶

QA time¶

to add things here¶

check columns match¶

view files¶

add annotations to git¶

add annotation folder and script to git¶

	infoOrgan	notes_anat	anatId_	anatName_	anatAnnotationStatus_	infoOrgan_new	comment_anat
0	Telencephalon - coronal section	so it seem coronal section of telencephalon wouldn't be different from an anatomy perspective - just cross sections of telencephalon instead of dissected	UBERON:0001893	telencephalon	perfect match
1	Telencephalon - whole telencephalon	whole telencephalon via dissection is more straightforward	UBERON:0001893	telencephalon	perfect match

	#libraryId	experimentId	platform	SRSId	anatId	anatName	cellTypeId	cellTypeName	stageId	stageName	url_GSM	infoOrgan	infoCellType_abInitio	infoCellType_inferred	clusterId	clusterName	infoStage	anatAnnotationStatus	cellTypeAnnotationStatus	stageAnnotationStatus	sex	strain	genotype	speciesId	RNAseqTags	protocol	protocolType	lib_name	sampleName	comment	condition	annotatorId	lastModificationDate	library_contruction_protocol	source_qc	lib_name_2	lib_name_3	source_name	individual	infoStage_2	infoStage_3
0	SRX24035191	SRP497465	NextSeq 2000	SRS20828625							https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8163334	Telencephalon - coronal section					15 days				M	Broiler		9031		10X Genomics	3'end	sample_2_coronal_section	SAMN40593971				28/02/2025	The telencephalon was dissected from brains of 15 days old domestic chickens. Two different sample types of chicken brain were used to isolate nuclei: dissected telencephalon snap-frozen in liquid nitrogen and a cryosectioned sample (12x 50 µm sections). The following procedure was followed to extract the nuclei: the tissues were resuspended in 300 µl nuclei lysis buffer comprising 10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Nonidet P40, 1 mM dithiothreitol and 1U/µl RNasin ribonuclease inhibitor (Promega) in nuclease-free water, pipeted until homogenized, 1 ml nuclei lysis buffer was added and samples were incubated on ice for 5 min. Nuclei were pelleted by centrifugation at 500 g for 5 min at 4°C and resuspended in 1x PBS, 1% BSA, 1U/µl RNasin ribonuclease inhibitor. Nuclei were centrifuged another time at 500 g for 5 min at 4°C and resuspended again in 1x PBS, 1% BSA, 1U/µl RNasin ribonuclease inhibitor. DAPI was added to the samples at a conc. of 0.5 µg/ml and incubated for 10 min. DAPI positive nuclei were sorted on a MA900 Sony cell sorter (Sony Biotechnology). The sorted nuclei were centrifuged at 500 g for 5 min at 4°C and resuspended in a 1 x nuclei buffer (10x Genomics) supplemented with 1 mM dithiothreitol and 1U/µl RNasin ribonuclease inhibitor. Single-cell libraries were generated using the 10X Chromium Single-Cell Instrument and NextGEM Single Cell Multiome ATAC+Gene Expression kit (10X Genomics) according to the manufacturer's protocol. In brief, the single nuclei of chicken brains were incubated for 60 min at 37°C with a transposase that fragments the DNA in open regions of the chromatin and adds adapter sequences to the ends of the DNA fragments. After generation of nanoliter-scale gel bead-in-emulsions (GEMs), GEMs were incubated in a C1000 Touch Thermal Cycler (Bio-Rad) under the following program: 37°C for 45 min, 25°C for 30 min, and hold at 4°C. Incubation of the GEMs produced 10x barcoded DNA from the transposed DNA (for ATAC) and 10x barcoded, full-length cDNA from poly-adenylated mRNA (for GEX). This was followed by a quenching step that stopped the reaction. After quenching, single-cell droplets were broken and the transposed DNA and full-length cDNA were isolated using Cleanup Mix containing Silane Dynabeads. To fill gaps and generate sufficient mass for library construction, the transposed DNA and cDNA were amplified via PCR: 72°C for 5 min; 98°C for 3 min; seven cycles of 98°C for 20 s, 63°C for 30 s, 72°C for 1 min; 72°C for 1 min; and hold at 4°C. The pre-amplified product was used as input for both ATAC library construction and cDNA amplification for gene expression library construction. Illumina P7 sequence and a sample index were added to the single-strand DNA during ATAC library construction via PCR: 98°C for 45 s; 7–9 cycles of 98°C for 20 s, 67°C for 30 s, 72°C for 20 s; 72°C for 1 min; and hold at 4°C. The sequencing-ready ATAC library was cleaned up with SPRIselect beads (Beckman Coulter). Barcoded, full-length pre-amplified cDNA was further amplified via PCR: 98°C for 3 min; 6–9 cycles of 98°C for 15 s, 63°C for 20 s, 72°C for 1 min; 72°C for 1 min; and hold at 4°C. Subsequently, the amplified cDNA was fragmented, end-repaired, A-tailed, and index adaptor ligated, with SPRIselect cleanup in between steps. The final gene expression library was amplified by PCR: 98°C for 45 s; 5–16 cycles of 98°C for 20 s, 54°C for 30 s, 72°C for 20 s, 72°C for 1 min; and hold at 4°C. The sequencing-ready GEX library was cleaned up with SPRIselect beads. Before sequencing, the fragment size of every library was analyzed using the Bioanalyzer high-sensitivity chip. All 10x scATAC libraries were sequenced on a NextSeq2000 instrument (Illumina) or on a NovaSeq6000 instrument (Illumina) with the following sequencing parameters: 51 bp read 1 – 8 bp index 1 – 24 bp index 2 – 51 bp read 2 and for 10x GEX libraries the following sequencing parameters were used: 28 bp read 1 - 10 bp index 1 - 10 bp index 2 - 90 bp read 2. OTHER: scRNA-seq + scATAC-seq				Telencephalon
1	SRX24035190	SRP497465	NextSeq 2000	SRS20828627							https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM8163333	Telencephalon - whole telencephalon					15 days				M	Broiler		9031		10X Genomics	3'end	sample_1_whole_telencephalon	SAMN40593972				28/02/2025	The telencephalon was dissected from brains of 15 days old domestic chickens. Two different sample types of chicken brain were used to isolate nuclei: dissected telencephalon snap-frozen in liquid nitrogen and a cryosectioned sample (12x 50 µm sections). The following procedure was followed to extract the nuclei: the tissues were resuspended in 300 µl nuclei lysis buffer comprising 10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Nonidet P40, 1 mM dithiothreitol and 1U/µl RNasin ribonuclease inhibitor (Promega) in nuclease-free water, pipeted until homogenized, 1 ml nuclei lysis buffer was added and samples were incubated on ice for 5 min. Nuclei were pelleted by centrifugation at 500 g for 5 min at 4°C and resuspended in 1x PBS, 1% BSA, 1U/µl RNasin ribonuclease inhibitor. Nuclei were centrifuged another time at 500 g for 5 min at 4°C and resuspended again in 1x PBS, 1% BSA, 1U/µl RNasin ribonuclease inhibitor. DAPI was added to the samples at a conc. of 0.5 µg/ml and incubated for 10 min. DAPI positive nuclei were sorted on a MA900 Sony cell sorter (Sony Biotechnology). The sorted nuclei were centrifuged at 500 g for 5 min at 4°C and resuspended in a 1 x nuclei buffer (10x Genomics) supplemented with 1 mM dithiothreitol and 1U/µl RNasin ribonuclease inhibitor. Single-cell libraries were generated using the 10X Chromium Single-Cell Instrument and NextGEM Single Cell Multiome ATAC+Gene Expression kit (10X Genomics) according to the manufacturer's protocol. In brief, the single nuclei of chicken brains were incubated for 60 min at 37°C with a transposase that fragments the DNA in open regions of the chromatin and adds adapter sequences to the ends of the DNA fragments. After generation of nanoliter-scale gel bead-in-emulsions (GEMs), GEMs were incubated in a C1000 Touch Thermal Cycler (Bio-Rad) under the following program: 37°C for 45 min, 25°C for 30 min, and hold at 4°C. Incubation of the GEMs produced 10x barcoded DNA from the transposed DNA (for ATAC) and 10x barcoded, full-length cDNA from poly-adenylated mRNA (for GEX). This was followed by a quenching step that stopped the reaction. After quenching, single-cell droplets were broken and the transposed DNA and full-length cDNA were isolated using Cleanup Mix containing Silane Dynabeads. To fill gaps and generate sufficient mass for library construction, the transposed DNA and cDNA were amplified via PCR: 72°C for 5 min; 98°C for 3 min; seven cycles of 98°C for 20 s, 63°C for 30 s, 72°C for 1 min; 72°C for 1 min; and hold at 4°C. The pre-amplified product was used as input for both ATAC library construction and cDNA amplification for gene expression library construction. Illumina P7 sequence and a sample index were added to the single-strand DNA during ATAC library construction via PCR: 98°C for 45 s; 7–9 cycles of 98°C for 20 s, 67°C for 30 s, 72°C for 20 s; 72°C for 1 min; and hold at 4°C. The sequencing-ready ATAC library was cleaned up with SPRIselect beads (Beckman Coulter). Barcoded, full-length pre-amplified cDNA was further amplified via PCR: 98°C for 3 min; 6–9 cycles of 98°C for 15 s, 63°C for 20 s, 72°C for 1 min; 72°C for 1 min; and hold at 4°C. Subsequently, the amplified cDNA was fragmented, end-repaired, A-tailed, and index adaptor ligated, with SPRIselect cleanup in between steps. The final gene expression library was amplified by PCR: 98°C for 45 s; 5–16 cycles of 98°C for 20 s, 54°C for 30 s, 72°C for 20 s, 72°C for 1 min; and hold at 4°C. The sequencing-ready GEX library was cleaned up with SPRIselect beads. Before sequencing, the fragment size of every library was analyzed using the Bioanalyzer high-sensitivity chip. All 10x scATAC libraries were sequenced on a NextSeq2000 instrument (Illumina) or on a NovaSeq6000 instrument (Illumina) with the following sequencing parameters: 51 bp read 1 – 8 bp index 1 – 24 bp index 2 – 51 bp read 2 and for 10x GEX libraries the following sequencing parameters were used: 28 bp read 1 - 10 bp index 1 - 10 bp index 2 - 90 bp read 2. OTHER: scRNA-seq + scATAC-seq				Telencephalon